Clustering videos using a self-supervised dnn

Abstract

Systems and methods are provided for clustering videos. The system accesses a plurality of content items, the plurality of content items comprising a first set of RGB video frames and a second set of optical flow frames corresponding to the first set of RGB video frames. The system processes the first set of RGB video frames by a first machine learning model to generate a first optimal assignment for the first set of RGB video frames, the first optimal assignment representing initial clustering of the first set of RGB video frames. The system generates an updated first optimal assignment for the first set of RGB video frames based on the first optimal assignment for the first set of RGB video frames and a second optimal assignment of the second set of optical flow frames, the second optimal assignment representing initial clustering of the second set of optical flow frames.

What is claimed is: 
     
         1 . A method comprising:
 accessing a plurality of content items, the plurality of content items comprising a first set of red, green, and blue (RGB) video frames and a second set of optical flow frames corresponding to the first set of RGB video frames;   processing the first set of RGB video frames by a first machine learning model to generate a first optimal assignment for the first set of RGB video frames, the first optimal assignment representing initial clustering of the first set of RGB video frames;   generating an updated first optimal assignment for the first set of RGB video frames based on both the first optimal assignment for the first set of RGB video frames and a second optimal assignment of the second set of optical flow frames, the second optimal assignment representing initial clustering of the second set of optical flow frames;   generating a first set of vectors in response to processing the first set of RGB video frames by the first machine learning model, the first set of vectors representing features of the first set of RGB video frames;   matching the first set of vectors to prototype cluster centers to generate the first optimal assignment for the first set of RGB video frames; and   applying a trained regularization term to equally space the prototype cluster centers.   
     
     
         2 . The method of  claim 1 , further comprising generating the second set of optical flow frames by:
 obtaining first and second video frames from the first set of RGB video frames;   computing a difference frame comprising motion information based on a deviation between the first and second video frames; and   storing the difference frame as one of the second set of optical flow frames.   
     
     
         3 . The method of  claim 1 , further comprising:
 processing the second set of optical flow frames by a second machine learning model to generate the second optimal assignment of the second set of optical flow frames.   
     
     
         4 . The method of  claim 3 , further comprising:
 generating an updated second optimal assignment for the second set of optical flow frames based on the first optimal assignment for the first set of RGB video frames.   
     
     
         5 . The method of  claim 4 , further comprising:
 computing a deviation between the updated first optimal assignment and the updated second optimal assignment; and   updating one or more parameters of at least one of the first or second machine learning models based on the computed deviation.   
     
     
         6 . The method of  claim 3 , wherein the first and second machine learning models each comprise a deep neural network (DNN) comprising one or more encoders. 
     
     
         7 . The method of  claim 3 , wherein the first machine learning model is trained to generate a first set of features corresponding to the first set of RGB video frames, and wherein the second machine learning model is trained to generate a second set of features corresponding to the second set of optical flow frames. 
     
     
         8 . The method of  claim 7 , wherein the first and second machine learning models are trained in an unsupervised manner end-to-end. 
     
     
         9 . The method of  claim 1 , wherein the initial clustering of the first set of RGB video frames represents different human activity depicted in the first set of RGB video frames. 
     
     
         10 . The method of  claim 1 , further comprising:
 generating a first set of vectors in response to processing the first set of RGB video frames by the first machine learning model, the first set of vectors representing features of the first set of RGB video frames; and   matching the first set of vectors to prototype cluster centers to generate the first optimal assignment for the first set of RGB video frames.   
     
     
         11 . The method of  claim 10 , further comprising applying a Sinkhorn-Knopp technique to match the first set of vectors to the prototype cluster centers. 
     
     
         12 . The method of  claim 10 , further comprising applying a trained regularization term to equally space the prototype cluster centers. 
     
     
         13 . The method of  claim 1 , wherein generating the updated first optimal assignment for the first set of RGB video frames comprises applying a k-means algorithm to the first optimal assignment for the first set of RGB video frames and the second optimal assignment of the second set of optical flow frames. 
     
     
         14 . A system comprising:
 at least one processor programmed to perform operations comprising:
 accessing a plurality of content items, the plurality of content items comprising a first set of red, green, and blue (RGB) video frames and a second set of optical flow frames corresponding to the first set of RGB video frames; 
 processing the first set of RGB video frames by a first machine learning model to generate a first optimal assignment for the first set of RGB video frames, the first optimal assignment representing initial clustering of the first set of RGB video frames; 
 generating an updated first optimal assignment for the first set of RGB video frames based on both the first optimal assignment for the first set of RGB video frames and a second optimal assignment of the second set of optical flow frames, the second optimal assignment representing initial clustering of the second set of optical flow frames; 
 generating a first set of vectors in response to processing the first set of RGB video frames by the first machine learning model, the first set of vectors representing features of the first set of RGB video frames; 
 matching the first set of vectors to prototype cluster centers to generate the first optimal assignment for the first set of RGB video frames; and 
 applying a trained regularization term to equally space the prototype cluster centers. 
   
     
     
         15 . The system of  claim 14 , the operations further comprising generating the second set of optical flow frames by:
 obtaining first and second video frames from the first set of RGB video frames;   computing a difference frame based on a deviation between the first and second video frames comprising motion information; and   storing the difference frame as one of the second set of optical flow frames.   
     
     
         16 . The system of  claim 15 , the operations further comprising:
 processing the second set of optical flow frames by a second machine learning model to generate the second optimal assignment of the second set of optical flow frames.   
     
     
         17 . The system of  claim 16 , the operations further comprising:
 generating an updated second optimal assignment for the second set of optical flow frames based on the first optimal assignment for the first set of RGB video frames.   
     
     
         18 . A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:
 accessing a plurality of content items, the plurality of content items comprising a first set of red, green, and blue (RGB) video frames and a second set of optical flow frames corresponding to the first set of RGB video frames;   processing the first set of RGB video frames by a first machine learning model to generate a first optimal assignment for the first set of RGB video frames, the first optimal assignment representing initial clustering of the first set of RGB video frames;   generating an updated first optimal assignment for the first set of RGB video frames based on both the first optimal assignment for the first set of RGB video frames and a second optimal assignment of the second set of optical flow frames, the second optimal assignment representing initial clustering of the second set of optical flow frames;   generating a first set of vectors in response to processing the first set of RGB video frames by the first machine learning model, the first set of vectors representing features of the first set of RGB video frames;   matching the first set of vectors to prototype cluster centers to generate the first optimal assignment for the first set of RGB video frames; and   applying a trained regularization term to equally space the prototype cluster centers.   
     
     
         19 . The non-transitory machine-readable storage medium of  claim 18 , the operations comprising generating the second set of optical flow frames by:
 obtaining first and second video frames from the first set of RGB video frames;   computing a difference frame comprising motion information based on a deviation between the first and second video frames; and   storing the difference frame as one of the second set of optical flow frames.   
     
     
         20 . The non-transitory machine-readable storage medium of  claim 19 , the operations comprising:
 processing the second set of optical flow frames by a second machine learning model to generate the second optimal assignment of the second set of optical flow frames.